Identify Membrane Proteins Research Articles

Pseudo Amino Acid Feature-based Protein Function Prediction using Support Vector Machine and K-Nearest Neighbors

Bioinformatics facing the vital challenge in protein function prediction due to protein data are available in primary structure, an amino acid sequence. Every protein cell sequence length and size are in different sequence order. Protein is available in 20 amino acid sequence alphabetic order; however, the corresponding information of the membrane protein sequence is insufficient to capture the function and structures of a protein from primary sequence datasets. A challenging task to correctly identify protein structure and function from amino acid sequence. The basic principle of PseAAC (Pseudo Amino Acid Composition) is to generate a discrete number of every protein samples. In each protein, sequence length varies due to protein functions. Some protein sequence length is less than 50, and some are large. Due to this, different sizes of the amino acid sample are chances to lose sequence order information. PseAAC feature generates a fixed size descriptor value in vector space to overcome sequence information loss and is used to further systematic evolution. Therefore machine learning computational tool synthesizes accurate identification of structure and function class of membrane protein. In this study, SVM (Support Vector Machine) and KNN (K-nearest neighbors) based prediction classifier used to identifying membrane protein and their types.

A 6-Membrane Protein Gene score for prognostic prediction of cytogenetically normal acute myeloid leukemia in multiple cohorts.

Background: Cytogenetically normal acute myeloid leukemia (CN-AML) is a large proportion of AMLs with diverse prognostic outcomes. Identifying membrane protein genes as prognostic factors to stratify CN-AML patients will be critical to improve their outcomes.Purpose: This study aims to identify prognostic factors to stratify CN-AML patients to choose better treatments and improve their outcomes.Methods: CN-AML data were from TCGA cohort (n = 79) and four GEO datasets. We identified independent prognostic genes by Cox regression and Kaplan-Meier methods, and constructed linear regression model using LASSO algorithm. The prediction error curve was calculated using R package “pec”.Results: Based on independent prognostic membrane genes, we constructed a regression model for CN-AML prognosis prediction: score = (0.0492 * CD52) - (0.0018 * CD96) + (0.0131 * EMP1) + (0.2058 * TSPAN2) + (0.0234 * STAB1) - (0.3658 * MBTPS1), which was named as MPG6 (6-Membrane Protein Gene) score. Tested in multiple CN-AML datasets, consistent results showed that CN-AML patients with high MPG6 score had poor survival, higher WBC count and shorter EFS. Comparing with other reported scoring models, the benchmark result of MPG6 achieved better association with survival in multiple cohorts. Moreover, by combining with other clinical indicators in CN-AML, MPG6 could improve the performance of survival prediction and serve as a robust prognostic factor.Conclusions: We identified the MPG6 score as a stable indicator with great potential for clinical application in risk stratification and outcome prediction in CN-AML.

Efficient profiling of detergent-assisted membrane proteome in cyanobacteria

Membrane proteins play key roles in cellular functions like transport of molecules, perception of environmental cues, and signal transduction into the intracellular compartment. However, the profiling of membrane proteins is still a daunting task because of the hydrophobicity, restricting our knowledge of membrane proteins. Thus, we attempted to develop a novel detergent-based approach to uncover membrane proteins using cyanobacteria. We investigated the effect of five different detergents on the profiling of the cyanobacterial membrane proteome. The application of either amidosulfobetaine-14 (ASB14) or N-lauroylsarcosine (NL) doubled the number of the identified integral membrane proteins compared with the control. Extraction with ASB14 increased the number of transmembrane helices over four times. The quantitative index (mol%) of membrane proteins also increased from 13 to 22% when ASB14, NL, and benzyldimethyl-n-hexadecylammonium chloride (BAC) were used. ASB14 treatment was particularly useful for identifying membrane proteins with higher molecular weights (Mr), and the addition of BAC to the cyanobacterial membrane could identify the membrane proteins with acidic isoelectric points (pI). To validate the efficiency of the detergent-based proteomics, the functional membrane protein complexes involved in energy metabolism were selected as an example and shown to be successful with the combined data of ASB14, NL, and zwittergent 3-10 (ZW3-10). Taken together, we suggest that the use of a specific detergent and the subsequent combination of proteome data is critical for detailed profiling of cyanobacterial functional membrane proteins.

Identification of membrane protein types via multivariate information fusion with Hilbert–Schmidt Independence Criterion

Membrane proteins perform a variety of functions vital to the survival of organisms, such as oxidoreductase, transferase or hydrolase. If the type of membrane protein can be detected, the function of protein can be quickly determined. Many existing computational methods not only use the autocorrelation function on the hydrophobicity index of amino acids, but also consider the evolutionary conservatism information of the primary protein sequences. In this study, we employ Average Blocks (AvBlock), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Histogram of Oriented Gradient (HOG) and Pseudo-PSSM (PsePSSM) to extract evolution characteristics from Position-Specific Score Matrix (PSSM). Then, we construct five kernels from above five corresponding feature sets. Finally, we propose a novel Multiple Kernel Support Vector Machine (MKSVM) classifier based on Hilbert Schmidt Independence Criterion (HSIC) to integrate five kernels for identifying membrane proteins. For the performance evaluation, our method is tested on four benchmark datasets of membrane proteins. The comparative results demonstrate that our prediction model achieves the best performance among all existing outstanding approaches.

Topological analysis of TMEM180, a newly identified membrane protein that is highly expressed in colorectal cancer cells

New target molecules for diagnosis of and drug development for colorectal cancer (CRC) are always in great demand. Previously, we identified a new colorectal cancer–specific protein, TMEM180, and successfully developed an anti-TMEM180 monoclonal antibody (mAb) for the diagnosis and treatment of CRC. Although TMEM180 is classified as a member of the cation symporter family and multi-pass membrane protein, little is known about its function. In this study, we examined topology of this membrane protein and analyzed its function. Using a homology model of human TMEM180, we experimentally determined that the protein has 12 transmembrane domains, and that its N-terminal and C-termini are exposed extracellularly. Moreover, we found that the putative cation-binding site of TMEM180 is conserved among orthologs, and that its position is similar to that of melibiose transporter MelB. These results suggest that TMEM180 acts as a cation symporter. Our topological analysis based on the homology model provides insight into functional and structural roles of TMEM180 that may help to elucidate the pathology of CRC.

In vivo CRISPR screening in CD8 T cells with AAV-Sleeping Beauty hybrid vectors identifies membrane targets for improving immunotherapy for glioblastoma.

Identification of T cell targets to improve immunotherapies is of prime interest. To facilitate large-scale CRISPR screens directly in T cells in vivo, here, we developed a hybrid genetic screening system with adeno associated virus (AAV) and the Sleeping Beauty (SB) transposon, where the transposon is nested in the viral vector. The approach enables efficient gene editing in primary murine T cells and genomic integration of the sgRNA cassette for screen readout. We performed focused in vivo AAV-SB-CRISPR screens in CD8+ T cells in mouse models of glioblastoma (GBM) and identified membrane protein targets. Adoptive transfer of CD8+ T cells with Pdia3, Mgat5, Emp1, or Lag3 gene editing enhance the survival of GBM-bearing mice in both syngeneic and TCR-transgenic models. Transcriptome profiling, single cell sequencing, cytokine assays, and T cell signaling analysis showed that Pdia3 editing in T cells enhances effector functions. Engineered PDIA3 mutant EGFRvIII chimeric antigen T cells are more potent in antigen-specific killing of human GBM cells.

Identifying Cell Surface Markers of Primary Neural Stem and Progenitor Cells by Metabolic Labeling of Sialoglycan.

Neural stem and progenitor cells (NSPCs) are the cellular basis for the complex structures and functions of the brain. They are located in specialized niches in vivo and can be isolated and expanded in vitro, serving as an important resource for cell transplantation to repair brain damage. However, NSPCs are heterogeneous and not clearly defined at the molecular level or purified due to a lack of specific cell surface markers. The protocol presented, which has been previously reported, combines a neural-endothelial co-culture system with a metabolic glycan labeling method to identify the surface sialoglycoproteome of primary NSPCs. The NSPC-endothelial co-culture system allows self-renewal and expansion of primary NSPCs in vitro, generating a sufficient number of NSPCs. Sialoglycans in cultured NSPCs are labeled using an unnatural sialic acid metabolic reporter with bioorthogonal functional groups. By comparing the sialoglycoproteome from self-renewing NSPCs expanded in an endothelial co-culture with differentiating neural culture, we identify a list of membrane proteins that are enriched in NSPCs. In detail, the protocol involves: 1) set-up of an NSPC-endothelial co-culture and NSPC differentiating culture; 2) labeling with azidosugar per-O-acetylated N-azidoacetylmannosamine (Ac4ManNAz); and 3) biotin conjugation to modified sialoglycan for imaging after fixation of neural culture or protein extraction from neural culture for mass spectrometry analysis. Then, the NSPC-enriched surface marker candidates are selected by comparative analysis of mass spectrometry data from both the expanded NSPC and differentiated neural cultures. This protocol is highly sensitive for identifying membrane proteins of low abundance in the starting materials, and it can be applied to marker discovery in other systems with appropriate modifications.

High throughput screening to identify host-factors involved in RSV fusion

Respiratory syncytial virus (RSV) is the major cause of childhood respiratory disease; however, currently there is no licenced vaccine available and the only therapeutic, a monoclonal antibody against the viral Fusion (F) protein, is expensive and applied sparingly. RSV particles enter cells by membrane fusion, orchestrated by F – a type I integral membrane protein. This process was recently shown to involve macropinocytosis of the particle. Separately, RSV can spread through induction of direct cell-cell fusion – again orchestrated by F. Little is currently known about the host-factors involved in regulating or inhibiting RSV F-mediated fusion. Here, using two different high-throughput screening approaches, we have identified host-factors involved in regulating RSV fusion. Using quantitative mass-spectrometry analysis of isolated cell membrane fractions from mock and RSV-infected cells we have identified membrane proteins which are differentially regulated during RSV infection. Furthermore, using lentiviral libraries expressing individual interferon stimulated genes (ISGs) from different mammalian species we have investigated ISG-mediated inhibition of RSV fusion. Our data provides important insights into host-factors involved in RSV spread, furthering our understanding of the fusion process and identifying potential targets for antiviral therapy.

Proteomic Sample Preparation through Extraction by Unspecific Adsorption on Silica Beads for ArgC-like Digestion.

Sample preparation for mass-spectrometry-based proteomic analyses usually requires intricate, multistep workflows that are often limited in capacity or suffer from sample loss. Here, we introduce a lean adsorption-based protocol (ABP) for the extraction of proteins from fresh cell lysates that enables us to modify and tag protein samples under harsh conditions, such as organic solvents, high salt concentrations, or low pH values. This offers high versatility while also reducing the required steps in the preparation process significantly. Protein identifications are slightly increased compared to traditional acetone precipitation followed by an in-solution digestion (AP/IS) or filter aided sample preparation (FASP) and proved complementary to both methods regarding proteome coverage. When combined with ArgC-like digestion, this approach delivered 5386 uniquely identified proteins, a substantial increase of 18.27% over tryptic digestion (4554), while decreasing spectra complexity due to a lower number of peptide to spectra matches per protein and the number of missed cleaved peptides. In addition, an increased number of identified membrane proteins and histones as well as improved fragmentation and intensity coverage were observed through comprehensive data analysis.

A proximity-tagging system to identify membrane protein-protein interactions.

The communication between cells and between cellular organelles is often controlled by the interaction of membrane proteins. Although many methods for the detection of protein-protein interactions (PPIs) exist, membrane PPIs remain difficult to detect. Here we developed a proximity-based tagging system, PUP-IT (pupylation-based interaction tagging), to identify membrane protein interactions. In this approach, a small protein tag, Pup, is applied to proteins that interact with a PafA-fused bait, enabling transient and weak interactions to be enriched and detected by mass spectrometry. Pup does not diffuse from the enzyme, which allows high-specificity labeling. We applied this approach to CD28, a critical costimulatory receptor for T lymphocyte activation, and identified known CD28 binding partners and multiple potential interacting proteins. In addition, we demonstrated that this method can identify the interaction between a cell surface receptor and its ligand.

TMEM16F/ANO6, a Ca2+-activated anion channel, is negatively regulated by the actin cytoskeleton and intracellular MgATP

Anoctamin 6 (ANO6/TMEM16F) is a recently identified membrane protein that has both phospholipid scramblase activity and anion channel function activated by relatively high [Ca2+]i. In addition to the low sensitivity to Ca2+, the activation of ANO6 Cl− conductance is very slow (>3–5 min to reach peak level at 10 μM [Ca2+]i), with subsequent inactivation. In a whole-cell patch clamp recording of ANO6 current (IANO6,w-c), disruption of the actin cytoskeleton with cytochalasin-D (cytoD) significantly accelerated the activation kinetics, while actin filament-stabilizing agents (phalloidin and jasplakinolide) commonly inhibited IANO6,w-c. Inside-out patch clamp recording of ANO6 (IANO6,i-o) showed immediate activation by raising [Ca2+]i. We also found that intracellular ATP (3 mM MgATP in pipette solution) decelerated the activation of IANO6,w-c, and also prevented the inactivation of IANO6,w-c. However, the addition of cytoD still accelerated both activation and inactivation of IANO6,w-c. We conclude that the actin cytoskeleton and intracellular ATP play major roles in the Ca2+-dependent activation and inactivation of IANO6,w-c, respectively.

Enhancing Membrane Protein Identification Using a Simplified Centrifugation and Detergent-Based Membrane Extraction Approach.

Membrane proteins may act as transporters, receptors, enzymes, and adhesion-anchors, accounting for nearly 70% of pharmaceutical drug targets. Difficulties in efficient enrichment, extraction, and solubilization still exist because of their relatively low abundance and poor solubility. A simplified membrane protein extraction approach with advantages of user-friendly sample processing procedures, good repeatability and significant effectiveness was developed in the current research for enhancing enrichment and identification of membrane proteins. This approach combining centrifugation and detergent along with LC-MS/MS successfully identified higher proportion of membrane proteins, integral proteins and transmembrane proteins in membrane fraction (76.6%, 48.1%, and 40.6%) than in total cell lysate (41.6%, 16.4%, and 13.5%), respectively. Moreover, our method tended to capture membrane proteins with high degree of hydrophobicity and number of transmembrane domains as 486 out of 2106 (23.0%) had GRAVY > 0 in membrane fraction, 488 out of 2106 (23.1%) had TMs ≥ 2. It also provided for improved identification of membrane proteins as more than 60.6% of the commonly identified membrane proteins in two cell samples were better identified in membrane fraction with higher sequence coverage. Data are available via ProteomeXchange with identifier PXD008456.

Screening and Scale-Up of GLUT Transporter Constructs Suitable for Biochemical and Structural Studies.

Identifying membrane proteins that can be produced and isolated in homogenous form in detergent is a lengthy trial-and-error process that can be facilitated by fluorescence-based screening approaches. We describe (1) the strategy and protocol of cloning by homologous recombination, (2) whole-cell and in-gel fluorescence measurements to estimate GLUT-GFP fusion protein yields, (3) use of size-exclusion chromatography monitored by fluorescence (FSEC) for assessing the homogeneity of the GLUT-GFP fusion proteins, and (4) the protocol for large-scale production and purification of the Bos taurus GLUT5 construct that enabled its crystal structure determination.

Screening and Identifying Membrane Proteins Favorable for Crystallization.

This unit addresses several critical challenges associated with membrane protein crystallography by screening membrane proteins from Escherichia coli, Saccharomyces cerevisiae, and Sus scrofa cerebral tissue for biochemical properties favorable for crystallization. First, a tissue sample or cell pellet is obtained. The cells are isolated, washed, and then lysed either by sonication, bead beating, or manual homogenization. Membrane proteins are fractionated from the lysates by centrifugation and solubilized in a mild detergent suitable for crystallization, such as n-dodecyl-β-maltoside (DDM). Detergent extracts are then centrifuged, heat precipitated, and filtered to remove insoluble, thermally unstable, and/or aggregated proteins. Samples are then prepared for analysis by mass spectrometry: proteins are precipitated by methanol/chloroform extraction and subjected to reduction, alkylation, and protease digestion. The resulting peptides are passed through a detergent removal column, desalted, rehydrated in 0.1% formic acid (v/v), and identified by LC-MS/MS. © 2017 by John Wiley & Sons, Inc.

Identification of membrane proteome of Paracoccidioides lutzii and its regulation by zinc.

Aim:During infection development in the host, Paracoccidioides spp. faces the deprivation of micronutrients, a mechanism called nutritional immunity. This condition induces the remodeling of proteins present in different metabolic pathways. Therefore, we attempted to identify membrane proteins and their regulation by zinc in Paracoccidioides lutzii.Materials & methods:Membranes enriched fraction of yeast cells of P. lutzii were isolated, purified and identified by 2D LC–MS/MS detection and database search.Results & conclusion:Zinc deprivation suppressed the expression of membrane proteins such as glycoproteins, those involved in cell wall synthesis and those related to oxidative phosphorylation. This is the first study describing membrane proteins and the effect of zinc deficiency in their regulation in one member of the genus Paracoccidioides.

Identification of protein targets in cerebral endothelial cells for brain arteriovenous malformation (AVMs) molecular therapies

BackgroundTo develop a new molecular targeted treatment for brain (AVMs), identification of membrane proteins that are localised on the AVM endothelium is crucial. Current treatment methods are surgery and radiosurgery. However, complete occlusion post radiosurgery are achieved within 3 years, while patient remain at risk of haemorrhage. This study aims to identify potential protein targets in AVM endothelial cells that discriminate these vessels from normal vessels; these proteins targets will be investigated for the molecular therapy of brain AVMs to promote rapid thrombosis after radiosurgery.MethodsWe employed in vitro biotinylation that we developed, and mass spectrometry to detect cell surface-exposed proteins in cultures of murine cerebral endothelial cells (bEnd.3). Two forms of mass spectrometry were applied (iTRAQ-MS and MSE) to identify and quantify membrane protein expression at various time-points following irradiation which simulates a radiosurgical treatment approach. Immunocytochemistry was used to confirm the expression of selected membrane proteins. ProteinPilot V4.0 software was used to analyse the iTRAQ-MS data and the MSE data was analysed using ProteinLynx Global Server version 2.5 software.ResultsThe proteomics data revealed several differentially expressed membrane proteins between irradiated and non-irradiated cells at specific time points, e.g. PECAM-1, cadherin-5, PDI, EPCR and integrins. Immunocytochemistry data confirmed the expression of these proteins.ConclusionCell surface protein biotinylation and proteomics analysis successfully identified membrane proteins from murine brain endothelial cells in response to irradiation. This work suggests potential target protein molecules for evaluation in animal models of brain-AVM.

Improving Escherichia coli membrane integrity and fatty acid production by expression tuning of FadL and OmpF

BackgroundConstruction of microbial biocatalysts for the production of biorenewables at economically viable yields and titers is frequently hampered by product toxicity. Membrane damage is often deemed as the principal mechanism of this toxicity, particularly in regards to decreased membrane integrity. Previous studies have attempted to engineer the membrane with the goal of increasing membrane integrity. However, most of these works focused on engineering of phospholipids and efforts to identify membrane proteins that can be targeted to improve fatty acid production have been unsuccessful.ResultsHere we show that deletion of outer membrane protein ompF significantly increased membrane integrity, fatty acid tolerance and fatty acid production, possibly due to prevention of re-entry of short chain fatty acids. In contrast, deletion of fadL resulted in significantly decreased membrane integrity and fatty acid production. Consistently, increased expression of fadL remarkably increased membrane integrity and fatty acid tolerance while also increasing the final fatty acid titer. This 34% increase in the final fatty acid titer was possibly due to increased membrane lipid biosynthesis. Tuning of fadL expression showed that there is a positive relationship between fadL abundance and fatty acid production. Combinatorial deletion of ompF and increased expression of fadL were found to have an additive role in increasing membrane integrity, and was associated with a 53% increase the fatty acid titer, to 2.3 g/L.ConclusionsThese results emphasize the importance of membrane proteins for maintaining membrane integrity and production of biorenewables, such as fatty acids, which expands the targets for membrane engineering.

Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC

This study investigates an efficient and accurate computational method for predicating mycobacterial membrane protein. Mycobacterium is a pathogenic bacterium which is the causative agent of tuberculosis and leprosy. The existing feature encoding algorithms for protein sequence representation such as composition and translation, and split amino acid composition cannot suitably express the mycobacterium membrane protein and their types due to biasness among different types. Therefore, in this study a novel un-biased dipeptide composition (Unb-DPC) method is proposed. The proposed encoding scheme has two advantages, first it avoid the biasness among the different mycobacterium membrane protein and their types. Secondly, the method is fast and preserves protein sequence structure information. The experimental results yield SVM based classification accurately of 97.1% for membrane protein types and 95.0% for discriminating mycobacterium membrane and non-membrane proteins by using jackknife cross validation test. The results exhibit that proposed model achieved significant predictive performance compared to the existing algorithms and will lead to develop a powerful tool for anti-mycobacterium drugs.

Mapping the membrane proteome of anaerobic gut fungi identifies a wealth of carbohydrate binding proteins and transporters

BackgroundEngineered cell factories that convert biomass into value-added compounds are emerging as a timely alternative to petroleum-based industries. Although often overlooked, integral membrane proteins such as solute transporters are pivotal for engineering efficient microbial chassis. Anaerobic gut fungi, adapted to degrade raw plant biomass in the intestines of herbivores, are a potential source of valuable transporters for biotechnology, yet very little is known about the membrane constituents of these non-conventional organisms. Here, we mined the transcriptome of three recently isolated strains of anaerobic fungi to identify membrane proteins responsible for sensing and transporting biomass hydrolysates within a competitive and rather extreme environment.ResultsUsing sequence analyses and homology, we identified membrane protein-coding sequences from assembled transcriptomes from three strains of anaerobic gut fungi: Neocallimastix californiae, Anaeromyces robustus, and Piromyces finnis. We identified nearly 2000 transporter components: about half of these are involved in the general secretory pathway and intracellular sorting of proteins; the rest are predicted to be small-solute transporters. Unexpectedly, we found a number of putative sugar binding proteins that are associated with prokaryotic uptake systems; and approximately 100 class C G-protein coupled receptors (GPCRs) with non-canonical putative sugar binding domains.ConclusionsWe report the first comprehensive characterization of the membrane protein machinery of biotechnologically relevant anaerobic gut fungi. Apart from identifying conserved machinery for protein sorting and secretion, we identify a large number of putative solute transporters that are of interest for biotechnological applications. Notably, our data suggests that the fungi display a plethora of carbohydrate binding domains at their surface, perhaps as a means to sense and sequester some of the sugars that their biomass degrading, extracellular enzymes produce.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-016-0611-7) contains supplementary material, which is available to authorized users.

Dissolving capability difference based sequential extraction: A versatile tool for in-depth membrane proteome analysis

Profiling membrane proteins would facilitate revealing disease mechanism and discovering new drug targets as they play essential roles in cellular signaling, substrate transport, and cell adhesion. However, the analysis of membrane proteins still remains a challenge due to their high hydrophobicity, as well as the suppression effect of high abundant soluble proteins. In this work, to achieve a membrane proteome profiling, a sample preparation strategy based on sequential extraction at the protein level assisted by a range of extraction reagents with different dissolving capabilities, followed by nano-RPLC-ESI-MS/MS analysis was developed and applied for HeLa cell line analysis. It was found that with progressively harsher extraction reagents (i.e., 2 M NaCl, 4 M urea, 0.1 M Na2CO3, and 10% 1-dodecyl-3- methyl-imidazolium chloride (C12ImCl) performed, much more high hydrophobic proteins and low abundant proteins were identified. With our developed strategy, 5553 of the identified proteins (4419 gene products) were annotated to be membrane proteins and 2573 proteins (2183 gene products) have at least one transmembrane domain, to our best knowledge, which is the most comprehensive membrane proteome dataset for HeLa cell line. Notably, 110 of the identified membrane proteins were discovered in the “missing proteins” list referred to those in the neXtProt database. All above results indicated that our strategy has great potential to tackle the difficult but relevant task of identifying and profiling membrane proteins.